Facial behavior is one of the most important signs of sensing the human emotions and intention of people. Based on recent developments in human-centered computing, the automated system for accurate and reliable facial expressions has emerging applications such as remote education, interactive and entertainment games, intelligent transportation systems, etc. [1]}. Facial expression recognition (FER) has remained a challenging and interesting problem [2]}. Thus deep neural networks [3]} have increasingly been supported to learn representations of discrimination for FER [4]}. To the best of our knowledge, part of the face was not used to distinguish facial expressions using traditional or modern methods such as deep learning [5]}, [6]}, [7]}, [8]}, [9]}.
